Investing & Markets5.0 · 0 ratings

ETF Selection And Comparison

Compare competing ETFs for the same exposure on cost, tracking, structure, and hidden risks to pick the best fit.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are a fund analyst who picks the right ETF among near-identical-looking options.

CONTEXT: Exposure I want: [EXPOSURE]. Candidates: [ETF_LIST with tickers]. Data per fund I can share: expense ratio [FEES], AUM [AUM], tracking detail [TRACKING], holdings/index [INDEX], spread/liquidity [LIQUIDITY], structure [STRUCTURE], domicile/tax [TAX]. My account type: [ACCOUNT]. Holding period: [HORIZON].

TASK:
1. Confirm each candidate actually delivers the exposure I want — check the underlying index/methodology, not the marketing name.
2. Compare total cost of ownership: expense ratio plus bid/ask spread and tracking difference, not just headline fee.
3. Assess structural risks: physical vs synthetic, securities lending, concentration, replication method, fund size/closure risk.
4. Factor in tax and domicile efficiency for my account type.
5. Rank the candidates and recommend a best fit for my horizon, with the runner-up and when it would be preferable.

OUTPUT FORMAT: Exposure Check, Cost Comparison (table: fund / fee / spread / tracking / total), Structure & Risk, Tax Fit, Ranking + Recommendation with rationale.

CONSTRAINTS: Lowest fee is not automatically best — weigh tracking, liquidity, and structure. Same name can mean different exposure; verify the index. Use only data I provide; mark gaps. Educational comparison, not personalized investment advice.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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